HapPenIng: Happen, Predict, Infer -- Event Series Completion in a Knowledge Graph

Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly. 1 Introduction Event series, such as sports tournaments, music festivals and political elections are sequences of recurring events. Prominent examples include the Wimbledon Championships, the Summer Olympic Games, the United States presidential elections and the International Semantic Web Conference. The provision of reliable reference sources for event series is of crucial importance for many real-world applications, for example in the context of Digital Humanities and Web Science research [7, 9, 25], as well as media analytics and digital journalism [15, 23].